cpa(SensoMineR)
cpa()所属R语言包：SensoMineR
Consumers' Preferences Analysis
消费者的偏好分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs preference mapping techniques based on multidimensional exploratory data analysis. This methodology is oriented towards consumers' preferences; here consumers are pictured according only to their preferences. In this manner, the distance between two consumers is very natural and easy to interpret, and a clustering of the consumers is also very easy to obtain.
执行偏好测绘技术多方面的探索性数据分析的基础上。这种方法是面向消费者的喜好，根据自己的喜好，在这里消费者被描绘。在这种方式下，消费者之间的距离是很自然的和易于理解的，一个聚类的消费者也很容易获得。
用法----------Usage----------
cpa(senso, hedo, coord=c(1,2), center = TRUE, scale = TRUE,
nb.clusters = 0, scale.unit = FALSE,
col = terrain.colors(45)[1:41])
参数----------Arguments----------
参数：senso
a data frame of dimension (p,k), where p is the number of products and k the number of sensory descriptors
一个数据框的尺寸（对，k），其中p是产品的数量和k感官的描述符的数目
参数：hedo
a data frame of dimension (p,j), where p is the number of products and j the number of consumers or panelists
的尺寸（对，j）的一个数据框，其中p是产品的数量和j的消费者或小组成员的数目
参数：coord
a length 2 vector specifying the components to plot
长度2矢量指定组件绘制
参数：center
boolean, if TRUE then data are mean centered
布尔值，如果为TRUE，则数据意味着中心
参数：scale
boolean, if TRUE then data are scaled to unit variance
布尔值，如果为TRUE，则数据缩放到单位方差
参数：nb.clusters
number of clusters to use (by default, 0 and the optimal numer of clusters is calculated
簇数（默认情况下，0和最佳numer聚类计算
参数：scale.unit
boolean, if TRUE then PCA is made on scaled data
布尔值，如果为真则PCA上规模数据
参数：col
color palette
调色板
Details
详细信息----------Details----------
This methodology is oriented towards consumers' preferences; here, consumers are pictured according only to their preferences. In this manner, the distance between two consumers is very natural and easy to interpret, and a clustering of the consumers is also very easy to obtain using a classic hierarchical clustering procedure performed on Euclidian distances with the Ward's minimum variance criterion. The originality of the representation is that the characteristics of the products are also superimposed to the former picture.
这种方法是面向消费者的喜好，在这里，消费者根据自己的喜好图。在这种方式下，消费者之间的距离是很自然的和易于理解的，一个聚类的消费者也很容易获得使用的是经典的层次聚类方法，欧几里得距离病房的最小方差准则进行。的代表性的独创性的产品的特性也会被叠加前的图片。
值----------Value----------
Return the following results:
返回以下结果：
参数：clusters
the cluster number allocated to each consumer
分配给每一个消费者的簇号
参数：result
the coordinates of the panelists, of the clusters, of the archetypes
的坐标的小组成员中，聚类，原型
参数：prod.clusters
a list with as many elements as there are clusters; each element of the list gathers the specific products for its corresponding cluster
一样多的元素有聚类的列表的列表的每个元素集为它的相应的聚类的特定产品
参数：desc.clusters
the correlation coefficients between the average hedonic scores per cluster and the sensory descriptors
享乐分数的平均每个聚类的感觉描述符之间的相关系数
A dendogram which highlight the clustering, a correlation circle that displays the hedonic scores, a graph of the consumers such as two consumers are all the more close that they do like the same products, as many graphs as there are variables: for a given variable, each consumer is colored according to the coefficient of correlation based on his hedonic scores and the variable.
一个dendogram突出的聚类，相关圆，显示享乐的分数，图的消费者，如两名消费者更近，他们喜欢同样的产品，为许多图形存在变数：对于一个给定的变量被着色，每个消费者根据他享乐分数和变量的基础上的相关系数。
（作者）----------Author(s)----------
Fran莽ois Husson <a href="mailto:Fran莽ois.Husson@agrocampus-ouest.fr">Fran莽ois.Husson@agrocampus-ouest.fr</a> <br>
S茅bastien L锚 <a href="mailto:Sebastien.Le@agrocampus-ouest.fr">Sebastien.Le@agrocampus-ouest.fr</a>
参考文献----------References----------
6th Pangborn sensory science symposium, August 7-11, 2005, Harrogate, UK.
实例----------Examples----------
## Not run: [＃不运行：]
data(cocktail)
res.cpa = cpa(cbind(compo.cocktail, senso.cocktail), hedo.cocktail)
## If you prefer a graph in black and white and with 3 clusters[＃如果你喜欢在黑色和白色，与3类图]
res.cpa = cpa(cbind(compo.cocktail, senso.cocktail), hedo.cocktail,
name.panelist = TRUE, col = gray((50:1)/50), nb.clusters = 3)
## End(Not run)[＃（不执行）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 00:56:51
